The invention relates generally to imaging systems and more specifically to a method and system for detecting and correcting edges in images.
Computer aided diagnosis (CAD), such as screening mammography and evaluation of other disease states or medical or physiological events, is typically based upon various types of analysis of a series of collected images. The collected images are analyzed and pathologies of concern are highlighted by the CAD algorithm. The results of CAD are then reviewed by radiologists for final diagnosis. As can be appreciated by those skilled in the art, certain subsequent imaging procedures may become feasible or may be recognized as desirable due to the improved management of data volume.
It should be noted that CAD may be utilized in any imaging modality, such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray systems, ultrasound systems, positron emission tomography (PET), and so forth. CAD algorithms in certain of these modalities may provide advantages over those in other modalities, depending upon the imaging capabilities of the modality, the tissue being imaged, and so forth.
Computed tomography, for example, is generally a diagnostic procedure in which cross-sectional images or slices are made by an X-ray system. The CT scanning procedure combines the use of a computer system and a rotating X-ray device to create detailed cross sectional images or “slices” of a patient's organs and other body parts. The imaging capabilities are physically similar to those of X-ray systems. MRI, ultrasound, PET, and other modalities similarly are adapted to imaging certain tissues or anatomies, and provide advantages to CAD in certain clinical applications.
Typically, the images produced by such imaging systems are required to be accurate which will lead to the right diagnosis. Many techniques are employed to improve the quality of the image and to extract features that provide a basis for further analysis. Edge detection is one such tool which is used to solve many high-level problems in computer vision, such as object recognition, stereo vision, image segmentation, and optical metrology.
It is desirable to accurately locate curved edges in images. The accurate location of curved edges is important as it is used in many image based sizing applications across the field of medicine and metrology. In most images, the action of imaging system point spread function, as well as procedures for removal of noise biases the location of curved edges. It is desirable to determine and correct such biases in the images so as to accurately locate edges and accurately size a feature of interest.
It is also desirable for edge detection algorithms to have low error rates. That is, the shape and location of edges in the image need to be detected accurately and/or false edges should not be detected. Furthermore, it is desirable for the edge detection algorithms to have good edge localization which means that the detected edge should be close to the true edge.
Therefore, there is a need for accurate and efficient edge detection algorithms which can determine and correct the bias existing in images and therefore accurately locate edges.